L. Lerman, G. Bontempi, and O. Markowitch, Power analysis attack: An approach based on machine learning, Int. J. Appl. Cryptol, vol.3, issue.2, pp.97-115, 2014.

S. Picek, A. Heuser, and S. Guilley, Template attack versus Bayes classifier, Journal of Cryptographic Engineering, vol.7, issue.4, pp.343-351, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01629884

E. Cagli, C. Dumas, and E. Prouff, Convolutional Neural Networks with Data Augmentation Against Jitter-Based Countermeasures-Profiling Attacks Without Preprocessing, Cryptographic Hardware and Embedded Systems-CHES 201719th International Conference, pp.45-68, 2017.

H. Maghrebi, T. Portigliatti, and E. Prouff, Breaking cryptographic implementations using deep learning techniques, Security, Privacy, and Applied Cryptography Engineering-6th International Conference, pp.3-26, 2016.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems 25, pp.1097-1105, 2012.

A. Heuser, O. Rioul, and S. Guilley, Good is Not Good Enough-Deriving Optimal Distinguishers from Communication Theory, Lecture Notes in Computer Science, vol.8731, 2014.

L. Lerman, R. Poussier, G. Bontempi, O. Markowitch, and F. Standaert, Template Attacks vs, Machine Learning Revisited (and the Curse of Dimensionality in SideChannel Analysis). In: COSADE 2015, pp.20-33, 2015.

W. Schindler, K. Lemke, and C. Paar, A Stochastic Model for Differential Side Channel Cryptanalysis, LNCS, vol.3659, pp.30-46, 2005.

O. Choudary and M. G. Kuhn, Efficient template attacks, Smart Card Research and Advanced Applications-12th International Conference, CARDIS 2013, vol.8419, pp.253-270, 2013.

A. Heuser and M. Zohner, Intelligent Machine Homicide-Breaking Cryptographic Devices Using Support Vector Machines, LNCS, vol.7275, pp.249-264, 2012.

G. Hospodar, B. Gierlichs, E. De-mulder, I. Verbauwhede, and J. Vandewalle, Machine learning in side-channel analysis: a first study, Journal of Cryptographic Engineering, vol.1, pp.293-302, 2011.

L. Lerman, G. Bontempi, and O. Markowitch, A machine learning approach against a masked AES-Reaching the limit of side-channel attacks with a learning model, J. Cryptographic Engineering, vol.5, issue.2, pp.123-139, 2015.

S. Picek, A. Heuser, A. Jovic, and A. Legay, Climbing down the hierarchy: Hierarchical classification for machine learning side-channel attacks, Progress in Cryptology-AFRICACRYPT 2017: 9th International Conference on Cryptology in Africa, pp.61-78, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01629878

S. Picek, A. Heuser, A. Jovic, S. A. Ludwig, S. Guilley et al., Side-channel analysis and machine learning: A practical perspective, 2017 International Joint Conference on Neural Networks, pp.4095-4102, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01628681

R. Gilmore, N. Hanley, and M. O'neill, Neural network based attack on a masked implementation of AES, 2015 IEEE International Symposium on Hardware Oriented Security and Trust (HOST), pp.106-111, 2015.

A. Heuser, S. Picek, S. Guilley, and N. Mentens, Lightweight ciphers and their sidechannel resilience, IEEE Transactions on Computers, issue.99, pp.1-1, 2017.
DOI : 10.1109/tc.2017.2757921
URL : https://hal.archives-ouvertes.fr/hal-01629886

S. Picek, I. P. Samiotis, A. Heuser, J. Kim, S. Bhasin et al., On the performance of convolutional neural networks for side-channel analysis, Cryptology ePrint Archive, 2018.
URL : https://hal.archives-ouvertes.fr/hal-02010591

E. Prouff, R. Strullu, R. Benadjila, E. Cagli, and C. Dumas, Study of deep learning techniques for side-channel analysis and introduction to ASCAD database, IACR Cryptology ePrint Archive, p.53, 2018.

S. Picek, A. Heuser, A. Jovic, S. Bhasin, and F. Regazzoni, The curse of class imbalance and conflicting metrics with machine learning for side-channel evaluations, Cryptology ePrint Archive, vol.476, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01935318

F. X. Standaert, T. Malkin, and M. Yung, A Unified Framework for the Analysis of Side-Channel Key Recovery Attacks, LNCS, vol.5479, pp.443-461, 2009.

Y. Lecun and Y. Bengio, Convolutional networks for images, speech, and time series, The handbook of brain theory and neural networks, vol.3361, 1995.

A. Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals et al., Wavenet: A generative model for raw audio, 2016.

K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, 2014.

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, 2016.

T. Kim, J. Lee, and J. Nam, Sample-level CNN architectures for music auto-tagging using raw waveforms, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.366-370, 2018.
DOI : 10.1109/icassp.2018.8462046
URL : http://arxiv.org/pdf/1710.10451

S. Ioffe and C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, Proceedings of the 32nd International Conference on Machine Learning, pp.448-456, 2015.

N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: a simple way to prevent neural networks from overfitting, Journal of Machine Learning Research, vol.15, issue.1, pp.1929-1958, 2014.

C. M. Bishop, Training with noise is equivalent to tikhonov regularization, Neural computation, vol.7, issue.1, pp.108-116, 1995.
DOI : 10.1162/neco.1995.7.1.108

S. Rifai, X. Glorot, Y. Bengio, and P. Vincent, Adding noise to the input of a model trained with a regularized objective, 2011.

D. P. Kingma and J. Ba, Adam: A method for stochastic optimization, 2014.

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang et al., Automatic differentiation in pytorch, 2017.

T. Paristech and S. Research, DPA Contest, pp.2013-2014

A. Moradi, S. Guilley, and A. Heuser, Detecting Hidden Leakages, 12th International Conference on Applied Cryptography and Network Security, vol.8479, 1013.
DOI : 10.1007/978-3-319-07536-5_20

J. Coron and I. Kizhvatov, An Efficient Method for Random Delay Generation in Embedded Software, Cryptographic Hardware and Embedded Systems-CHES 2009, 11th International Workshop, pp.156-170, 2009.

L. Breiman, Random Forests, Machine Learning, vol.45, issue.1, pp.5-32, 2001.

R. Simon, Resampling strategies for model assessment and selection. In: Fundamentals of data mining in genomics and proteomics, pp.173-186, 2007.
DOI : 10.1007/978-0-387-47509-7_8

D. H. Wolpert, The Lack of a Priori Distinctions Between Learning Algorithms, Neural Comput, vol.8, issue.7, pp.1341-1390, 1996.